Which integrations are available for this server?

Provides real-time pricing, cost estimation, and budget tracking for Google models such as the Gemini Pro and Gemini Flash series. Provides real-time pricing, cost estimation, and budget tracking for OpenAI models including the GPT-5.2, GPT-4.1, and o-series.

How do I use AgentCost MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AgentCost MCP Server What's the cheapest model for summarizing a 10,000 token document right now?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🤖 AgentCost MCP Server

Cost awareness for AI agents. Know what you're spending before the invoice shows up.

An MCP (Model Context Protocol) server that gives any AI agent real-time access to model pricing, cost estimation, budget checking, and model comparison. Built by an agent, for agents.

Why?

AI agents are flying blind on costs. They pick models without knowing the price, run tasks without budget awareness, and generate surprise bills. AgentCost fixes this by giving agents the tools to understand and optimize their own spending.

Tools

Tool	Description
`estimate_cost`	Estimate cost for a model + token count before making the call
`compare_models`	Compare costs across models, get cheapest/best-value/best-quality picks
`check_budget`	Check if usage fits a daily budget, get smart switch suggestions
`find_cheapest`	Find cheapest model for a task (coding, reasoning, writing, etc.)
`list_models`	Browse all 20+ models across 7 providers with pricing
`get_model`	Deep-dive on a specific model with reference costs

Quick Start

Install

npm install -g agentcost-mcp

Use with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "agentcost": {
      "command": "agentcost-mcp"
    }
  }
}

Use with any MCP client

agentcost-mcp  # Runs on stdio

Example: Agent Self-Optimization

An agent can call these tools to make smarter decisions:

Agent: "I need to process 50 customer emails. Let me check the cost first."

→ estimate_cost(model_id="anthropic/claude-sonnet-4", input_tokens=2000, output_tokens=500)
→ Result: $0.0135 per email, $0.675 total

Agent: "That's reasonable. But let me see if there's something cheaper..."

→ compare_models(input_tokens=2000, output_tokens=500, task="classification", min_quality=70)
→ Recommendation: "GPT-4.1 Nano ($0.0006/email) for classification. 98% cheaper."

Agent: "Perfect. I'll use Nano for classification, Sonnet for the complex replies."

Models Covered (March 2026)

Anthropic: Claude Opus 4, Sonnet 4, Haiku 3.5
OpenAI: GPT-5.2, GPT-5.2 Codex, GPT-4.1, GPT-4.1 Mini/Nano, o3, o4-mini
Google: Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 3 Pro (Preview)
DeepSeek: V3, R1
xAI: Grok 4
Mistral: Mistral Large, Codestral

Prices updated from official provider pages. Open an issue if something's outdated.

Agent Labs

Built by Agent Labs — tools built BY agents, FOR agents.

Part of the Powered By Piland portfolio. Because agents deserve infrastructure too.

License

MIT

This server cannot be installed

-

security - not tested

F

license - not found

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

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AgentCost MCP Server